Data is sourced from World Data Bank, Census, and US Bureau of Labor Force Statistics. The data was narrowed down to include countries depending on their development indicators.
The least developed nations - Yemen and Afghanistan; developing nations - India and Azerbaijan; developed nations - United States.
import pandas as pd
import altair as alt
from IPython.display import HTML
import matplotlib.pyplot as plt
import geopandas
alt.data_transformers.enable('default', max_rows=None)
DataTransformerRegistry.enable('default')
jobs = pd.read_csv('JobsData.csv')
parliament = pd.read_csv('Par_Women_Data.csv')
women_wage_perc = pd.read_excel('wage_per_occupation.xlsx', sheet_name="Table 14")
lp = pd.read_csv("Labor Force Participation Rate of Mothers and Fathers by Age of Youngest Child.csv",
skiprows=1)
world_data = pd.read_csv("WDIData.csv")
mortality = pd.read_csv("MaternalMortalityData.csv")
inequality = pd.read_csv("gender-inequality-index-from-the-human-development-report.csv")
jobs = jobs.rename(columns = {"Indicator Name":"Variables"})
jobs.head(3)
| Country Name | Country Code | Variables | Indicator Code | 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | ... | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Arab World | ARB | Access to electricity (% of population) | EG.ELC.ACCS.ZS | 74.384239 | 74.382220 | 74.313160 | 75.349325 | 75.788522 | 76.214138 | ... | 84.735723 | 85.432827 | 85.189815 | 86.136134 | 86.782683 | 87.288244 | 88.389705 | 88.076774 | 88.517967 | 88.768654 |
| 1 | Arab World | ARB | Adolescent fertility rate (births per 1,000 wo... | SP.ADO.TFRT | 69.467160 | 68.211985 | 67.314595 | 65.256059 | 63.177552 | 60.907902 | ... | 50.543387 | 50.316994 | 50.104610 | 49.900118 | 49.723757 | 49.539074 | 49.111244 | 48.647539 | 48.114552 | 47.440069 |
| 2 | Arab World | ARB | Age dependency ratio (% of working-age populat... | SP.POP.DPND | 87.481340 | 86.726178 | 86.058118 | 84.906750 | 83.598142 | 81.946419 | ... | 65.275452 | 64.235293 | 63.365027 | 62.694715 | 62.341696 | 62.168854 | 62.118188 | 62.089858 | 62.017234 | 62.057475 |
3 rows × 31 columns
Dropping unnecessary columns and extracting only the percentage of Male and female employment in three sectors: \
1. Agriculture \
2. Industry \
3. Servicesjob_list_of_values = ["Employment in agriculture (% of total employment) (modeled ILO estimate)",
"Employment in agriculture, female (% of female employment) (modeled ILO estimate)",
"Employment in agriculture, male (% of male employment) (modeled ILO estimate)",
"Employment in industry (% of total employment) (modeled ILO estimate)",
"Employment in industry, female (% of female employment) (modeled ILO estimate)",
"Employment in industry, male (% of male employment) (modeled ILO estimate)",
"Employment in services (% of total employment) (modeled ILO estimate)",
"Employment in services, female (% of female employment) (modeled ILO estimate)",
"Employment in services, male (% of male employment) (modeled ILO estimate)",
"Labor force with advanced education, female (% of female working-age population with advanced education)",
"Labor force with basic education, female (% of female working-age population with basic education)",
"Labor force with intermediate education, female (% of female working-age population with intermediate education)",
"Labor force participation rate, female (% of female population ages 15+) (modeled ILO estimate)",
"Fertility rate, total (births per woman)",
"Literacy rate, adult female (% of females ages 15 and above)",
"Literacy rate, adult male (% of males ages 15 and above)",
"Self-employed, female (% of female employment) (modeled ILO estimate)",
"Self-employed, male (% of male employment) (modeled ILO estimate)",
]
jobs_df = jobs[jobs['Variables'].isin(job_list_of_values)]
jobs_df_small = jobs_df.reset_index()
jobs_df_small = jobs_df_small.drop(columns = ['Indicator Code','index'])
jobs_dfp = jobs_df_small.pivot(index='Variables', columns=['Country Name', 'Country Code']).T
jDF = jobs_dfp
jDF = jobs_dfp.rename(columns={"Employment in agriculture (% of total employment) (modeled ILO estimate)":"Agriculture_Total",
"Employment in agriculture, female (% of female employment) (modeled ILO estimate)":"Agriculture_Female",
"Employment in agriculture, male (% of male employment) (modeled ILO estimate)":"Agriculture_Male",
"Employment in industry (% of total employment) (modeled ILO estimate)":"Industry_Total",
"Employment in industry, female (% of female employment) (modeled ILO estimate)":"Industry_Female",
"Employment in industry, male (% of male employment) (modeled ILO estimate)":"Industry_Male",
"Employment in services (% of total employment) (modeled ILO estimate)":"Service_Total",
"Employment in services, female (% of female employment) (modeled ILO estimate)":"Service_Female",
"Employment in services, male (% of male employment) (modeled ILO estimate)":"Service_Male",
"Labor force with advanced education, female (% of female working-age population with advanced education)":"lab_AdvEdu_F",
"Labor force with basic education, female (% of female working-age population with basic education)":"lab_BasicEdu_F",
"Labor force with intermediate education, female (% of female working-age population with intermediate education)":"lab_intEdu_F",
"Labor force participation rate, female (% of female population ages 15+) (modeled ILO estimate)":"lab_part_F",
"Fertility rate, total (births per woman)":'Fertility',
"Literacy rate, adult female (% of females ages 15 and above)":'lit_F',
"Literacy rate, adult male (% of males ages 15 and above)":'lit_m',
"Self-employed, female (% of female employment) (modeled ILO estimate)":'self_Emp_F',
"Self-employed, male (% of male employment) (modeled ILO estimate)":'self_Emp_M'})
jDF.reset_index(inplace=True)
jDF.head()
| Variables | level_0 | Country Name | Country Code | Agriculture_Total | Agriculture_Female | Agriculture_Male | Industry_Total | Industry_Female | Industry_Male | Service_Total | ... | Service_Male | Fertility | lab_part_F | lab_AdvEdu_F | lab_BasicEdu_F | lab_intEdu_F | lit_F | lit_m | self_Emp_F | self_Emp_M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1990 | Arab World | ARB | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 5.206192 | 19.248613 | NaN | NaN | NaN | 40.99125 | 67.10404 | NaN | NaN |
| 1 | 1990 | East Asia & Pacific | EAS | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 2.497818 | 65.853601 | NaN | NaN | NaN | 74.78902 | 89.09240 | NaN | NaN |
| 2 | 1990 | East Asia & Pacific (excluding high income) | EAP | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 2.617091 | 68.498566 | NaN | NaN | NaN | 71.50912 | 87.87152 | NaN | NaN |
| 3 | 1990 | Euro area | EMU | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 1.534158 | 42.175977 | NaN | NaN | NaN | 96.76738 | 98.02343 | NaN | NaN |
| 4 | 1990 | Europe & Central Asia | ECS | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 1.957998 | 49.363324 | NaN | NaN | NaN | 95.64146 | 98.23361 | NaN | NaN |
5 rows × 21 columns
#renaming columns with appropriate names
jDF = jDF.rename(columns={'level_0':'Year',
"Country Name":"Country",
"Country Code":"CODE"})
#creating a list of year values
years = jDF['Year'].unique() # get unique field values
years = list(filter(lambda x: x > '2000', years)) # filter out None values
years.sort() # sort alphabetically
#binding values to drop-down
input_dropdown = alt.binding_select(options=years)
selectYear = alt.selection_point(
name='Select',
fields=['Year'],
value='2016',
bind=input_dropdown
#bind=alt.binding_range(min=1990, max=2016)
)
# display(HTML("""
# <style>
# form.vega-bindings {
# position: absolute;
# left: 0px;
# top: 0px;
# }
# </style>
# """))
#renaming legend names appropriately
legend_labels = ("datum.label == 'Agriculture_Female' ? 'Agriculture' : datum.label == 'Industry_Female' ? 'Industry' : 'Service'")
axis_labels = ("datum.label == 'Agriculture_Female' ? 'Female' : datum.label == 'Industry_Female' ? 'Female' : datum.label == 'Service_Female' ? 'Female': 'Male'")
#selection of color palette
color_category =['#3A2A51','#52A675','#FF595E'] #3 distinct
color_category1_light = ['#3A2A51','#BFAED5'] #2 lighter shade of 1 category color
color_category2_light = ['#52A675','#9FD0B4']
color_category3_light = ['#FF595E','#FFADB0']
heatmap = ['#3A2A51', '#FFC2C4']
heatmap1 = ['#FFC2C4','#3A2A51']
color_two_category = ['#3A2A51','#FF595E'] #2 distinct
#['#6A4C93','#1982C4','#FF924C']
#['#FF6B6B','#4ECDC4','#1A535C']#, '#638ccc'] #distinct; category
#['#000075','#f58231','#800000']
#choosing a stac bar visual
stackedbar = alt.Chart(jDF).mark_bar().add_params(selectYear).transform_filter(selectYear
).transform_fold(
['Agriculture_Female','Industry_Female','Service_Female']
).transform_filter(alt.FieldOneOfPredicate(field='Country',
oneOf=['India','Azerbaijan','United States',
'Afghanistan','Yemen, Rep.']) #'Yemen, Rep.'
).encode(
alt.Y('Country:N',
sort=['Afghanistan','Yemen, Rep.','India','Azerbaijan','United States'], title=None),
alt.X('value:Q',
title="Female share(%)", axis=alt.Axis(tickMinStep = 100),
scale= alt.Scale(domain=[0,100])),
alt.Color('key:N',
legend=alt.Legend(orient='right', titleOrient='top',
title='Employment Sector',labelExpr=legend_labels),
scale=alt.Scale(#domain=['Agriculture_Female','Industry_Female','Service_Female'],
range= color_category)),
alt.Order('key:N', sort='ascending'),
alt.Tooltip('value:Q',format='.1f')
).properties(
width = 750,
height = 120,
title = 'Share of Female Employment in Sectors(%)'
)
text = alt.Chart(jDF).mark_text(color='white',align='center',dx=-14,dy=0,fontSize=11
).transform_filter(
selectYear
).transform_fold(
['Agriculture_Female','Industry_Female','Service_Female']
).transform_filter(alt.FieldOneOfPredicate(field='Country',
oneOf=['India','Azerbaijan','United States',
'Afghanistan','Yemen, Rep.'])
).encode(
alt.Y('Country:N',sort=['Afghanistan','Yemen, Rep.','India','Azerbaijan','United States']),
alt.X('value:Q', stack='zero', scale= alt.Scale(domain=[0,100])),
alt.Text('value:N',format='.1f'),
alt.Order('key:N', sort='ascending'),
)
stackedbarsector = alt.layer(
stackedbar,text
).resolve_scale(
color='independent'
)
agri = alt.layer(
alt.Chart().mark_bar().transform_fold(
['Agriculture_Male','Agriculture_Female']
).encode(
alt.Y('key:N',stack='zero',axis=alt.Axis(labelExpr=axis_labels), title = None),
alt.X('value:Q',
title = None, axis=None,
# axis=alt.Axis(tickMinStep = 100),
scale=alt.Scale(domain=[0,100])),
alt.Color('key:N',scale=alt.Scale(range=color_category1_light),legend=None),
alt.Tooltip('value:Q',format='.1f')
)
,
alt.Chart().mark_text(color='black',align='center',dx=9.5,dy=0,fontSize=10
).transform_fold(
['Agriculture_Male','Agriculture_Female']
).encode(
alt.Y('key:N',stack='zero', title = None),
alt.X('value:Q',stack='zero', title = None),
alt.Text('value:N',format='.1f')
)
).properties(
width = 130,
height = 50
).facet(
data=jDF,
columns=5,
column =alt.Column('Country:N', title='Male and Female Share in Employment Sectors(%)',
header=alt.Header(titleFontSize=15, labelFontSize=12),
sort=['Afghanistan','Yemen, Rep.','India','Azerbaijan','United States'])
)
indu = alt.layer(
alt.Chart().mark_bar().transform_fold(
['Industry_Male','Industry_Female']
).encode(
alt.Y('key:N',stack='zero', axis=alt.Axis(labelExpr=axis_labels),title = None),
alt.X('value:Q',title = None, axis=None,
# axis=alt.Axis(tickMinStep = 100),
scale=alt.Scale(domain=[0,100])),
alt.Color('key:N',scale=alt.Scale(range=color_category2_light),legend=None),
alt.Tooltip('value:Q',format='.1f')
)
,
alt.Chart().mark_text(color='black',align='center',dx=12,dy=0,fontSize=10
).transform_fold(
['Industry_Male','Industry_Female']
).encode(
alt.Y('key:N',stack='zero', title = None),
alt.X('value:Q',stack='zero', title = None),
alt.Text('value:N',format='.1f')
)
).properties(
width = 130,
height = 50
).facet(
data=jDF,
columns=5,
column =alt.Column('Country:N',title=None,header=alt.Header(labels=False),
sort=['Afghanistan','Yemen, Rep.','India','Azerbaijan','United States'])
)
serv = alt.layer(
alt.Chart().mark_bar().transform_fold(
['Service_Male','Service_Female']
).encode(
alt.Y('key:N',stack='zero', axis=alt.Axis(labelExpr=axis_labels),title = None),
alt.X('value:Q',title = None, axis=None,
#axis=alt.Axis(tickMinStep = 100),
scale=alt.Scale(domain=[0,100])),
alt.Color('key:N',scale=alt.Scale(range=color_category3_light),legend=None),
alt.Tooltip('value:Q',format='.1f')
)
,
alt.Chart().mark_text(color='black',align='center',dx=-2,dy=0,fontSize=10,
).transform_fold(
['Service_Male','Service_Female']
).encode(
alt.Y('key:N',stack='zero', title = None),
alt.X('value:Q',stack='zero'),
alt.Text('value:N',format='.1f')
)
).properties(
width = 130,
height = 50,
).facet(
data=jDF,
columns=5,
column =alt.Column('Country:N',title=None,header=alt.Header(labels=False),
sort=['Afghanistan','Yemen, Rep.','India','Azerbaijan','United States'])
)
employment_sector = alt.vconcat(stackedbarsector , agri , indu, serv
).resolve_scale(
color='independent'
).transform_filter(
alt.FieldOneOfPredicate(field='Country', oneOf=['Afghanistan','India','Azerbaijan','United States','Yemen, Rep.'])
).add_params(selectYear).transform_filter(selectYear
).configure_title(
anchor='middle',
fontSize = 15
).configure_axis(
labelFontSize=12,
titleFontSize=12
).configure_legend(
labelFontSize=12,
titleFontSize =12,
strokeColor='gray',
fillColor='#EEEEEE',
padding=5,
cornerRadius=10,
orient='bottom-right'
).configure_view(stroke=None)
employment_sector
Female labor force participation is one of the key drivers in the country's economic development. The visual on top shows the percentage of women's share in 2016 by each employment sector for five countries. The series of smaller bar plots show the same, between males and females, in each industry, country-wise. These sectors are gender-disaggregated data and are a broad classification from the world data bank.
The stacked bar plot visual is indicative that the agriculture sector in a developed nation like the United States shows minor percentages; less than 1% of females from the US are employed in Agriculture, whereas 90.86% of them are in the Service sectors.
This is indicative that the US imports more agriculture products while putting its workforce in service sectors. As with developing or least developed nations, more than 50% of women are in the Agriculture sector. Over the last 15 years, this trend has been different for each of these countries, mainly influenced by economic and political factors.
The industry sector includes occupations requiring more physical strength; evidently, percentages of males are more in this sector. Female percentage shares in the service sector have improved considerably for developing nations, while the US still tops over the years. As it is a well-developed nation, opportunities given to women in the employment sector seem fair.
Exploring Parliament dataset
parliament.head()
| Year | Azerbaijan | Afghanistan | India | Yemen, Rep. | United States | World | |
|---|---|---|---|---|---|---|---|
| 0 | 2020 | 17.355372 | 27.016129 | 14.364641 | 0.332226 | 27.464789 | 25.580431 |
| 1 | 2019 | 16.806723 | 27.868852 | 14.391144 | 0.332226 | 23.433875 | 24.636604 |
| 2 | 2018 | 16.800000 | NaN | 11.808118 | 0.000000 | 23.502304 | 24.097878 |
| 3 | 2017 | 16.800000 | 27.710843 | 11.808118 | 0.000000 | 19.354839 | 23.590337 |
| 4 | 2016 | 16.800000 | 27.710843 | 11.970534 | 0.000000 | 19.168591 | 23.091367 |
line = alt.Chart(parliament).mark_line(point=True).transform_fold(
['Azerbaijan','United States','India','Afghanistan','World']).encode(
alt.X('Year:N', stack=None),
alt.Y('value:Q',
impute=alt.ImputeParams(method='mean'),
axis=alt.Axis(tickMinStep = 5),
scale=alt.Scale(domain=[0,30]),
title = '% of Women in Parliament'),
alt.Color('key:N'),
alt.Tooltip('value:Q')
).properties(
title ='Women % in Parliament over the years',
width=700
)
Choosing heatmap
parl_hm = alt.Chart(parliament).mark_rect().transform_fold(
['Azerbaijan','United States','India','Afghanistan','World']).encode(
alt.X('Year:N'),
alt.Y('key:N',sort=['Afghanistan','India','Azerbaijan','United States','World'], title=None),
alt.Color('value:Q',
scale=alt.Scale(range=heatmap1),
legend=alt.Legend(orient='right', titleOrient='top',
title='%')),
tooltip= alt.Tooltip('value:Q', format='.1f')
#alt.Size('value:Q')
).properties(
width= 750,
height=220,
title ='Women Share(%) in Parliament over the years'
).transform_filter(
'datum.Year > 2000'
).configure_title(
anchor='middle',
fontSize = 15
).configure_axis(
labelFontSize=12,
labelAngle=0,
titleFontSize=12
).configure_legend(
labelFontSize=9,
titleFontSize =12,
strokeColor='gray',
fillColor='#EEEEEE',
padding=5,
cornerRadius=10,
orient='bottom-right'
)
parl_hm
As years progress, women are securing more seats in the parliament. However, the rise of the percentages in the last 20 years is only 11%, 14% (2001) to 25%(2020) world average.
Afghanistan has a higher proportion than the United States; this does not mean that Afghanistan is moving toward equal representation, but rather that the United States ranks below a nation with a high GI index.
Although more women in Afghanistan can hold seats in government parliament, this doesn't translate to power. Several other factors show that Afghan women are mistreated. Time will tell if the percentage reaches even 50% in these countries.
labor_parent=lp[:4]
labor_parent = labor_parent.rename(columns={"Age of youngest child ":"child_age"})
labor_parent=pd.melt(labor_parent,id_vars=['child_age'],var_name='metrics', value_name='values')
labor_parent.head()
| child_age | metrics | values | |
|---|---|---|---|
| 0 | under 3 years | Mothers | 63.3 |
| 1 | 3 to 5 years | Mothers | 69.0 |
| 2 | 6 to 17 years | Mothers | 75.4 |
| 3 | under 18 years | Mothers | 71.2 |
| 4 | under 3 years | Fathers | 93.5 |
parentperc = alt.Chart(labor_parent).mark_bar().encode(
alt.Y('values:Q', title='Percent %'),
x = alt.X("metrics:N", title=None, axis=None),
color=alt.Color('metrics:N', scale=alt.Scale(range =heatmap), title='Parent'),
tooltip = ['values'],
column=alt.Column('child_age:N',title=("Percentage of Parent returning to Workforce by Age of the Youngest child"),
sort=["under 3 years", "3 to 5 years", "6 to 17 years","under 18 years"])
).transform_filter(
'datum.child_age != "under 18 years"'
).properties(
height = 400,
width=150
).configure_axis(
labelFontSize=12,
titleFontSize=12
).configure_title(
anchor='middle',
fontSize = 15
).configure_header(
titleFontSize=15,
labelFontSize=12
).configure_legend(
labelFontSize=10,
titleFontSize =12,
strokeColor='gray',
fillColor='#EEEEEE',
padding=5,
cornerRadius=10,
orient='right'
)
parentperc
The goal of this visual is to address the issue of the unequal dedication of years for parenting. If both parents make the decision to have a child, does time for parenting lie evenly on the parent's shoulders?
It appears that women with younger-aged children are less likely to be in the work market, and as the child grows, they tend to return to the labor force. However, the presence of a new child does not affect men's careers, as the highest labor force is when the child is under age 3.
Is this fact being considered in the future when a woman has a career gap on her resume or is it being treated as a lack of career experience? In a world where balance is not maintained in child care, there should be balance in future opportunities.
#Wage per Occupation Data Manipulation
occupation = pd.read_excel('wage_per_occupation.xlsx', sheet_name="Table 2")
occupation = occupation[3:]
data=occupation.reset_index()
data = data[4:]
data.columns = ['new_col1','Occupation', 'Number of workers/total', 'Median weekly earnings/total',
'Standard error of median/total', 'Number of workers/women',
'Median weekly earnings/women', 'Standard error of median/women',
'Number of workers/men','Median weekly earnings/men','Standard error of median/men',
"Women's earnings as a percentage of men's"]
data = data.reset_index()
data = data.drop(columns=['new_col1'])
occup_data = pd.wide_to_long(data,
stubnames=['Number of workers', 'Median weekly earnings','Standard error of median'],
i='index', j='group',
sep='/', suffix=r'\w+')
occup_data = occup_data.reset_index()
occup_data = occup_data.drop(columns=['index'])
occup_data = occup_data.rename(columns={"Women's earnings as a percentage of men's":'women_earn_percentage',
"Occupation":"occupation",
"Number of workers":'num_work',
"Median weekly earnings":'median_week_earn',
"Standard error of median":'std_error_med'})
# filter missing/invalid values
occup_data = occup_data[(occup_data['women_earn_percentage'] != '–') & (occup_data['group'] != 'total')]
occup_data.fillna(value = -1, inplace = True)
occup_data = occup_data[(occup_data['occupation']!= -1) & (occup_data['median_week_earn'] != -1) ]
occup_data
| group | occupation | women_earn_percentage | num_work | median_week_earn | std_error_med | |
|---|---|---|---|---|---|---|
| 598 | women | Management, professional, and related occupations | 73.8 | 25933 | 1164 | 4 |
| 599 | women | Management, business, and financial operations... | 76.4 | 9729 | 1274 | 12 |
| 600 | women | Management occupations | 77.5 | 5747 | 1347 | 12 |
| 601 | women | Chief executives | 75.6 | 363 | 2051 | 91 |
| 602 | women | General and operations managers | 80.5 | 281 | 1241 | 30 |
| ... | ... | ... | ... | ... | ... | ... |
| 1763 | men | Bus drivers, transit and intercity | 102.2 | 89 | 774 | 54 |
| 1764 | men | Driver/sales workers and truck drivers | 72.7 | 2409 | 916 | 14 |
| 1783 | men | Laborers and freight, stock, and material move... | 88.5 | 1268 | 672 | 9 |
| 1785 | men | Packers and packagers, hand | 90.1 | 205 | 604 | 8 |
| 1786 | men | Stockers and order fillers | 95.7 | 714 | 602 | 8 |
298 rows × 6 columns
# Wage Gap Bar Chart
bar_chart = alt.Chart(occup_data).mark_bar().transform_calculate(
wage_gap = 'datum.women_earn_percentage - 100',
gender_high_pay = 'datum.wage_gap > 0 ? "women earn more": "men earn more"'
).encode(
x=alt.X("occupation:N", title ='Occupation', axis = None),
y=alt.Y("wage_gap:Q",title ='Wage gap in %'),
tooltip = ['occupation','women_earn_percentage'],
color=alt.Color('gender_high_pay:N', scale=alt.Scale(range =heatmap), title=None)
).properties(title = 'Women Wage Gap per Occupation',width=1000)
bar_chart_wage_gap = bar_chart.properties(
height = 400,
width=900
).configure_axis(
labelFontSize=12,
titleFontSize=12
).configure_title(
anchor='middle',
fontSize = 15
).configure_header(
titleFontSize=15,
labelFontSize=12
).configure_legend(
labelFontSize=10,
titleFontSize =12,
strokeColor='gray',
fillColor='#EEEEEE',
padding=5,
cornerRadius=10,
orient='bottom-right'
)
#bar_chart_wage_gap
These visual carries a huge message, as women have higher paychecks only in 5 out of 149 occupations, and the following are the list of those occupations: Bus Drivers, Fast food and counter workers, Office and Administrative workers, producers and directors, and Wholesale and Retail buyers.
The highest wage gap is seen in the Legal occupations field, which is one of the highest-paid occupations. The height of bar charts where women are getting paid more is significantly less than of opposite ones. This means that even if women are paid more in those occupations, the difference in pay is not that huge. This visual carries fair analysis since the median earnings were classified by each occupation
#color palette list
color_5_category =['#3A2A51','#FF7075' ,"#FFD35C",'#52A675',"#FFADB0"] #3 distinct
W = 430
sort_cty=['Yemen, Rep.','Afghanistan','India','Azerbaijan','United States']
# filter by country
jobs = pd.read_csv("JobsData.csv")
inequality = pd.read_csv("gender-inequality-index-from-the-human-development-report.csv")
inequality_cty =inequality[inequality["Entity"].isin(["India","United States"
,"Yemen, Rep."
,"Afghanistan"
,"Azerbaijan"
])]
inequality_2005_2021 = inequality_cty[inequality_cty["Year"]>= 2005]
inequality_2021 = inequality_cty[inequality_cty["Year"]== 2021]
inequality_2021
inequality_world_2021 = inequality[inequality["Year"]== 2021]
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world.head()
C:\Users\rrads\AppData\Local\Temp\ipykernel_27524\850357869.py:1: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
| pop_est | continent | name | iso_a3 | gdp_md_est | geometry | |
|---|---|---|---|---|---|---|
| 0 | 889953.0 | Oceania | Fiji | FJI | 5496 | MULTIPOLYGON (((180.00000 -16.06713, 180.00000... |
| 1 | 58005463.0 | Africa | Tanzania | TZA | 63177 | POLYGON ((33.90371 -0.95000, 34.07262 -1.05982... |
| 2 | 603253.0 | Africa | W. Sahara | ESH | 907 | POLYGON ((-8.66559 27.65643, -8.66512 27.58948... |
| 3 | 37589262.0 | North America | Canada | CAN | 1736425 | MULTIPOLYGON (((-122.84000 49.00000, -122.9742... |
| 4 | 328239523.0 | North America | United States of America | USA | 21433226 | MULTIPOLYGON (((-122.84000 49.00000, -120.0000... |
merge_DF = pd.merge(world, inequality_world_2021, left_on='iso_a3', right_on='Code')
merge_DF.columns =['pop_est', 'continent', 'name', 'iso_a3', 'gdp_md_est', 'geometry',
'Entity', 'Code', 'Year',
'GDI']
GDI_Trend = ( alt.Chart(inequality_2005_2021).mark_line(
).encode(
alt.X("Year:N" )
,alt.Y( "Gender Inequality Index:Q")
# ,column = "Name:N"
# longitude='longitude:Q', # apply the field named 'longitude' to the longitude channel
# latitude='latitude:Q' # apply the field named 'latitude' to the latitude channel
,color = alt.Color("Entity:N"
, scale = alt.Scale(range = color_5_category)
,sort = sort_cty)
# , tooltip = ["name" , "GDI"]
)).properties(
width=W,
# / height=500
title = "Gender Inequality Index"
)
GDI_bar = ( alt.Chart(inequality_2021).mark_bar(
).encode(
alt.X("Entity:N" ,sort = sort_cty )
,alt.Y( "Gender Inequality Index:Q")
# ,column = "Name:N"
# longitude='longitude:Q', # apply the field named 'longitude' to the longitude channel
# latitude='latitude:Q' # apply the field named 'latitude' to the latitude channel
,color = alt.Color("Entity:N"
, scale = alt.Scale(range = color_5_category)
,sort = sort_cty
, legend=alt.Legend(orient='top', titleOrient='left',
title='Country'
))
# , tooltip = ["name" , "GDI"]
)).properties(
width=W,
# / height=500
title = "Gender Inequality Index - 2021"
)
(GDI_Trend | GDI_bar).configure_view(
stroke=None
).configure_legend(
labelFontSize=12,
titleFontSize =12,
strokeColor='gray',
fillColor='#EEEEEE',
padding=5,
cornerRadius=10,
orient='top-right'
)
Jobs = jobs[['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code',
'2011']]
Jobs.columns = Jobs.columns.astype(str)
Stats5countries = Jobs[Jobs["Country Name"].isin(sort_cty)]
Female_secodary_enrolment = Stats5countries[Stats5countries["Indicator Code"].isin([
"SE.SEC.ENRR.FE"])]
Secondary_bar = alt.Chart(Female_secodary_enrolment).mark_line( stroke = "#65605D" , color = "#1B3727" ).encode(
alt.X("Country Name:N", title = None ,sort=sort_cty, axis=alt.Axis(labels=False))
, alt.Y("2011:Q" , title = "School enrollment, secondary, female (% gross)" , scale=alt.Scale(domain=[0,100]))
)
Fertility = pd.read_csv("Adolescent_fertilirt.csv")
Fertility_2017 = Fertility[Fertility["Year"] == 2017]
Fertility_2017
| Year | Adolescent fertility rate (births per 1,000 women ages 15-19) | Country | |
|---|---|---|---|
| 3 | 2017 | 55.838 | Azerbaijan |
| 24 | 2017 | 68.957 | Afghanistan |
| 45 | 2017 | 60.352 | Yemen, Rep. |
| 66 | 2017 | 13.177 | India |
| 87 | 2017 | 19.860 | United States |
fertility_bar = alt.Chart(Fertility_2017).mark_bar().encode(
alt.X("Country:N", title = None ,sort=sort_cty)
, alt.Y("Adolescent fertility rate (births per 1,000 women ages 15-19):Q"
, title = "Adolescent fertility rate" )
,alt.Color("Country:N" )
).transform_filter("datum.Country != 'World'").properties(width =W , title = "Adolescent fertility rate (births per 1,000 women ages 15-19) - 2017")
P1 = (fertility_bar + Secondary_bar.encode(
alt.Y("2011:Q" ,title = None , axis=alt.Axis(labels=False)))).resolve_scale(
y="independent"
, x = "independent"
).properties(width =W
)
mortality = pd.read_csv("Maternal_Mortality_ratio.csv")
mortality_2017 = mortality[mortality["Year"] == 2017]
mortality_2017
| Year | Country | Maternal mortality ratio (per 100,000 live births) | |
|---|---|---|---|
| 0 | 2017 | World | 211 |
| 18 | 2017 | Afghanistan | 638 |
| 36 | 2017 | Azerbaijan | 26 |
| 54 | 2017 | India | 145 |
| 72 | 2017 | Yemen, Rep. | 164 |
| 90 | 2017 | United States | 19 |
mortality_bar = alt.Chart(mortality_2017).mark_bar().encode(
alt.X("Country:N", title = None ,sort=sort_cty )
, alt.Y("Maternal mortality ratio (per 100,000 live births):Q" , title = "Maternal mortality ratio" )
,alt.Color("Country:N" , legend = None , scale = alt.Scale(range = color_5_category))
).transform_filter("datum.Country != 'World'").properties(width =W , title = "Maternal mortality ratio (per 100,000 live births)")
mortality_bar
p2 = (mortality_bar + Secondary_bar
).resolve_scale(
y="independent"
, x = "independent"
).properties(width =W , title = "Maternal mortality ratio (per 100,000 live births) - 2017")
mortality_trend = alt.Chart(mortality).mark_line().encode(
alt.X("Year:N")
,alt.Y("Maternal mortality ratio (per 100,000 live births)" , title ="Maternal mortality ratio")
,alt.Color("Country"
, scale = alt.Scale(range = color_5_category)
, legend=alt.Legend(orient='top', titleOrient='left',
title='Country'
))).transform_filter("datum.Country != 'World'"
).properties(width =W , title = "Trend of Maternal mortality ratio (per 100,000 live births)")
Fertility_trend = alt.Chart(Fertility).mark_line(
).encode(
alt.X("Year:N")
,alt.Y("Adolescent fertility rate (births per 1,000 women ages 15-19)"
, title = "Adolescent fertility rate")
,alt.Color("Country")
).transform_filter("datum.Country != 'World'"
).transform_filter("datum.Year <='2017'"
).properties(width =W
, title ="Trend of Adolescent fertility rate (births per 1,000 women ages 15-19)" )
Ferti_Mortalilty = (( Fertility_trend | mortality_trend) & (P1 | p2)
).resolve_scale(color = "independent").configure_legend(
labelFontSize=12,
titleFontSize =12,
strokeColor='gray',
fillColor='#EEEEEE',
padding=5,
cornerRadius=10,
orient='top-right'
).configure_axis(
labelFontSize=10,
titleFontSize=10
,labelAngle=0
).configure_title(
anchor='middle',
fontSize = 12
)
employment_sector
parl_hm
Ferti_Mortalilty
parentperc
bar_chart_wage_gap
The project aimed to explore the main aspects driving the Gender inequality index. Some explored questions included factors such as mortality ratio, school enrollment of females, fertility rate, women in parliament, women returning to work after a child, women in employment sectors, and wage differences between genders. Some findings from our exploration;
Secondary education provided for females can lead to improvement in terms of maternal mortality and adolescent fertility. Wage difference analysis suggests that women sacrifice their careers and dedicate time to childcare, whereas men’s employment trend stays almost unaffected.
Furthermore, analysis of the trend of high-earning males in certain occupations remains unchanged, and only 3-4% of occupations for women are paid higher than men. Strengthening the collective power of women in leadership is perhaps the answer to bridging gaps.
The world has an average of 25% women’s share in parliament seats. It is a hopeful sign that there will be an increase in the coming years, and the world will move towards lower disparities between genders.